This is a competing continuation application for a research effort whose long term goal is to improve neurosurgical image-guidance by updating the spatial relationships between images acquired preoperatively and the current surgical field. Our approach is based on constructing estimates of brain deformation from modeling methods that assimilate surface/subsurface intra-operative data obtained from readily-available lowcost tracking technologies. We have demonstrated that this conceptual framework can be implemented in the OR and that the results are promising in terms of providing the neurosurgeon with a more accurate correspondence between the dynamically evolving surgical volume and the images obtained preoperatively. However, continued success requires an increased emphasis on system integration because the overall process involves a complex interaction of pre- and intra-operative images/data which must occur with an efficiency and accuracy that meets the temporal and spatial demands necessary to improve surgical decisions in the OR. Our specific aims for continuation have been formulated from this perspective and represent a series of studies, essential for further development, optimization, validation and evaluation of a data-driven model-based updating concept for improved neurosurgical image-guidance. Specifically, during the next funding period we propose (1) to extend modeling capabilities to incorporate advanced mechanical behaviors associated with (i) parenchymal distention, (ii) dural membrane effects, (iii) cranial wall interactions and (iv) volume compliance related to fluid compartments; (2) to extend, optimize and evaluate data assimilation techniques for integrating intra-operative tissue motion information including (i) forced displacement methods, (ii) weighted basis solutions and (iii) adjoint equation schemes; (3) to advance, automate and accelerate the acquisition and processing of preoperative and intra-operative data for model assimilation and image updating including techniques for (i) feature identification and tracking, (ii) displacement mapping, (iii) tissue property estimation and (iv) software optimization; and (4) to validate and evaluate the data-driven model-based updating technique through a series of animal and clinical surgeries that include the deformation processes associated with (i) cortical surface motion, (ii) tissue retraction and (iii) tissue resection.